Privacy-Preserving Tree-Based Inference with Fully Homomorphic Encryption

ePrint Report: Privacy-Preserving Tree-Based Inference with Fully Homomorphic Encryption

Jordan Frery, Andrei Stoian, Roman Bredehoft, Luis Montero, Celia Kherfallah, Benoit Chevallier-Mames, Arthur Meyre

Privacy enhancing technologies (PETs) have been proposed as a way to protect the privacy of data while still allowing for data analysis. In this work, we focus on Fully Homomorphic Encryption (FHE), a powerful tool that allows for arbitrary computations to be performed on encrypted data. FHE has received lots of attention in the past few years …

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